Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism

dc.contributor.author	Budak, Umit
dc.contributor.author	cibuk, Musa
dc.contributor.author	Comert, Zafer
dc.contributor.author	Sengur, Abdulkadir
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2021
dc.identifier.issn	0897-1889
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9864
dc.identifier.uri	https://doi.org/10.1007/s10278-021-00434-5
dc.description.abstract	Coronavirus (COVID-19) is a pandemic, which caused suddenly unexplained pneumonia cases and caused a devastating effect on global public health. Computerized tomography (CT) is one of the most effective tools for COVID-19 screening. Since some specific pa
dc.language.iso	English
dc.publisher	Sprınger
dc.rights	Bronze, Green Published
dc.source	Journal Of Dıgıtal Imagıng
dc.title	Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism
dc.type	Article
dc.identifier.issue	2
dc.identifier.startpage	263
dc.identifier.endpage	272
dc.identifier.doi	10.1007/s10278-021-00434-5
dc.identifier.wos	WOS:000625586900001
dc.identifier.volume	34

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